research-article

Open access

Mixture of volumetric primitives for efficient neural rendering

Authors:

Stephen Lombardi,

Gabriel Schwartz,

Michael Zollhoefer,

Jason SaragihAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 40, Issue 4

Article No.: 59, Pages 1 - 13

https://doi.org/10.1145/3450626.3459863

Published: 19 July 2021 Publication History

Abstract

Real-time rendering and animation of humans is a core function in games, movies, and telepresence applications. Existing methods have a number of drawbacks we aim to address with our work. Triangle meshes have difficulty modeling thin structures like hair, volumetric representations like Neural Volumes are too low-resolution given a reasonable memory budget, and high-resolution implicit representations like Neural Radiance Fields are too slow for use in real-time applications. We present Mixture of Volumetric Primitives (MVP), a representation for rendering dynamic 3D content that combines the completeness of volumetric representations with the efficiency of primitive-based rendering, e.g., point-based or mesh-based methods. Our approach achieves this by leveraging spatially shared computation with a convolutional architecture and by minimizing computation in empty regions of space with volumetric primitives that can move to cover only occupied regions. Our parameterization supports the integration of correspondence and tracking constraints, while being robust to areas where classical tracking fails, such as around thin or translucent structures and areas with large topological variability. MVP is a hybrid that generalizes both volumetric and primitive-based representations. Through a series of extensive experiments we demonstrate that it inherits the strengths of each, while avoiding many of their limitations. We also compare our approach to several state-of-the-art methods and demonstrate that MVP produces superior results in terms of quality and runtime performance.

Supplementary Material

VTT File (3450626.3459863.vtt)

Download
19.01 KB

ZIP File (a59-lombardi.zip)

a59-lombardi.zip

Download
965.58 KB

MP4 File (a59-lombardi.mp4)

Download
40.38 MB

MP4 File (3450626.3459863.mp4)

Presentation.

Download
280.80 MB

References

[1]

Rameen Abdal, Yipeng Qin, and Peter Wonka. 2019. Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV).

[2]

Kara-Ali Aliev, Artem Sevastopolsky, Maria Kolos, Dmitry Ulyanov, and Victor Lempitsky. 2019. Neural point-based graphics. arXiv preprint arXiv:1906.08240 (2019).

[3]

Benjamin Attal, Selena Ling, Aaron Gokaslan, Christian Richardt, and James Tompkin. 2020. MatryODShka: Real-time 6DoF Video View Synthesis using Multi-Sphere Images. arXiv preprint arXiv:2008.06534 (2020).

[4]

Michael Broxton, John Flynn, Ryan Overbeck, Daniel Erickson, Peter Hedman, Matthew Duvall, Jason Dourgarian, Jay Busch, Matt Whalen, and Paul Debevec. 2020. Immersive Light Field Video with a Layered Mesh Representation. ACM Trans. Graph. 39, 4, Article 86 (July 2020), 15 pages.

Digital Library

[5]

Rohan Chabra, Jan Eric Lenssen, Eddy Ilg, Tanner Schmidt, Julian Straub, Steven Lovegrove, and Richard Newcombe. 2020. Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction. arXiv preprint arXiv:2003.10983 (2020).

[6]

Wenzheng Chen, Huan Ling, Jun Gao, Edward Smith, Jaakko Lehtinen, Alec Jacobson, and Sanja Fidler. 2019. Learning to predict 3d objects with an interpolation-based differentiable renderer. In Advances in Neural Information Processing Systems. 9609--9619.

[7]

Christopher B Choy, Danfei Xu, Jun Young Gwak, Kevin Chen, and Silvio Savarese. 2016. 3d-r2n2: A unified approach for single and multi-view 3d object reconstruction. In European conference on computer vision. Springer, 628--644.

[8]

Yilun Du, Yinan Zhang, Hong-Xing Yu, Joshua B. Tenenbaum, and Jiajun Wu. 2020. Neural Radiance Flow for 4D View Synthesis and Video Processing. arXiv preprint arXiv:2012.09790 (2020).

[9]

Guy Gafni, Justus Thies, Michael Zollhöfer, and Matthias Nießner. 2020. Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction. https://arxiv.org/abs/2012.03065 (2020).

[10]

Chen Gao, Yichang Shih, Wei-Sheng Lai, Chia-Kai Liang, and Jia-Bin Huang. 2020. Portrait Neural Radiance Fields from a Single Image. https://arxiv.org/abs/2012.05903 (2020).

[11]

Kyle Genova, Forrester Cole, Aaron Maschinot, Aaron Sarna, Daniel Vlasic, and William T Freeman. 2018. Unsupervised training for 3d morphable model regression. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 8377--8386.

[12]

Kyle Genova, Forrester Cole, Avneesh Sud, Aaron Sarna, and Thomas Funkhouser. 2020. Local Deep Implicit Functions for 3D Shape. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 4857--4866.

[13]

Eldar Insafutdinov and Alexey Dosovitskiy. 2018. Unsupervised Learning of Shape and Pose with Differentiable Point Clouds. In Advances in Neural Information Processing Systems 31 (NIPS 2018), S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett (Eds.). Curran Associates, Montréal, Canada, 2804--2814.

[14]

Chiyu Jiang, Avneesh Sud, Ameesh Makadia, Jingwei Huang, Matthias Nießner, and Thomas Funkhouser. 2020. Local implicit grid representations for 3d scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 6001--6010.

[15]

Abhishek Kar, Christian Häne, and Jitendra Malik. 2017. Learning a multi-view stereo machine. In Advances in neural information processing systems. 365--376.

[16]

Tero Karras and Timo Aila. 2013. Fast Parallel Construction of High-Quality Bounding Volume Hierarchies. In Proceedings of the 5th High-Performance Graphics Conference (Anaheim, California) (HPG '13). Association for Computing Machinery, New York, NY, USA, 89--99.

Digital Library

[17]

Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. 2020. Analyzing and Improving the Image Quality of StyleGAN. In Proc. CVPR.

[18]

Hiroharu Kato, Yoshitaka Ushiku, and Tatsuya Harada. 2018. Neural 3d mesh renderer. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]

Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7--9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http://arxiv.org/abs/1412.6980

[20]

Diederik P Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013).

[21]

Maria Kolos, Artem Sevastopolsky, and Victor Lempitsky. 2020. TRANSPR: Transparency Ray-Accumulating Neural 3D Scene Point Renderer. arXiv:2009.02819

[22]

Samuli Laine, Janne Hellsten, Tero Karras, Yeongho Seol, Jaakko Lehtinen, and Timo Aila. 2020. Modular Primitives for High-Performance Differentiable Rendering. ACM Transactions on Graphics 39, 6 (2020).

Digital Library

[23]

Christoph Lassner and Michael Zollhöfer. 2020. Pulsar: Efficient Sphere-based Neural Rendering. arXiv:2004.07484 [cs.GR]

[24]

Zhengqi Li, Simon Niklaus, Noah Snavely, and Oliver Wang. 2020. Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes. https://arxiv.org/abs/2011.13084 (2020).

[25]

Chen-Hsuan Lin, Chen Kong, and Simon Lucey. 2018. Learning efficient point cloud generation for dense 3d object reconstruction. In Thirty-Second AAAI Conference on Artificial Intelligence.

[26]

Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, and Christian Theobalt. 2020. Neural Sparse Voxel Fields. arXiv:2007.11571

[27]

Shichen Liu, Tianye Li, Weikai Chen, and Hao Li. 2019. Soft rasterizer: A differentiable renderer for image-based 3d reasoning. In Proceedings of the IEEE International Conference on Computer Vision. 7708--7717.

[28]

Stephen Lombardi, Jason Saragih, Tomas Simon, and Yaser Sheikh. 2018. Deep Appearance Models for Face Rendering. ACM Trans. Graph. 37, 4, Article 68 (July 2018), 13 pages.

Digital Library

[29]

Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, and Yaser Sheikh. 2019. Neural Volumes: Learning Dynamic Renderable Volumes from Images. ACM Trans. Graph. 38, 4, Article 65 (July 2019), 14 pages.

Digital Library

[30]

Matthew M Loper and Michael J Black. 2014. OpenDR: An approximate differentiable renderer. In European Conference on Computer Vision. Springer, 154--169.

[31]

Feng Liu Xiaoming Liu Luan Tran. 2019. Towards High-fidelity Nonlinear 3D Face Morphoable Model. In IEEE Computer Vision and Pattern Recognition (CVPR).

[32]

Ricardo Martin-Brualla, Noha Radwan, Mehdi Sajjadi, Jonathan Barron, Alexey Dosovitskiy, and Daniel Duckworth. 2020. NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections. https://arxiv.org/abs/2008.02268 (2020).

[33]

Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. 2019. Occupancy Networks: Learning 3D Reconstruction in Function Space. In Conference on Computer Vision and Pattern Recognition (CVPR).

[34]

Moustafa Meshry, Dan B Goldman, Sameh Khamis, Hugues Hoppe, Rohit Pandey, Noah Snavely, and Ricardo Martin-Brualla. 2019. Neural rerendering in the wild. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 6878--6887.

[35]

Ben Mildenhall, Pratul P. Srinivasan, Rodrigo Ortiz-Cayon, Nima Khademi Kalantari, Ravi Ramamoorthi, Ren Ng, and Abhishek Kar. 2019. Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines. ACM Transactions on Graphics (TOG) (2019).

[36]

Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. 2020. Nerf: Representing scenes as neural radiance fields for view synthesis. arXiv preprint arXiv:2003.08934 (2020).

[37]

Sergio Orts-Escolano, Christoph Rhemann, SeanFanello, Wayne Chang, Adarsh Kowdle, Yury Degtyarev, David Kim, Philip L. Davidson, Sameh Khamis, Mingsong Dou, Vladimir Tankovich, Charles Loop, Qin Cai, Philip A. Chou, Sarah Mennicken, Julien Valentin, Vivek Pradeep, Shenlong Wang, Sing Bing Kang, Pushmeet Kohli, Yuliya Lutchyn, Cem Keskin, and Shahram Izadi. 2016. Holoportation: Virtual 3D Teleportation in Real-Time. In Proceedings of the 29th Annual Symposium on User Interface Software and Technology (Tokyo, Japan) (UIST '16). Association for Computing Machinery, New York, NY, USA, 741--754.

Digital Library

[38]

Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Love-grove. 2019. Deepsdf: Learning continuous signed distance functions for shape representation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 165--174.

[39]

Keunhong Park, Utkarsh Sinha, Jonathan Barron, Sofien Bouaziz, Dan Goldman, Steven Seitz, and Ricardo Martin-Brualla. 2020. Deformable Neural Radiance Fields. https://arxiv.org/abs/2011.12948 (2020).

[40]

Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger. 2020. Convolutional Occupancy Networks. In European Conference on Computer Vision (ECCV).

[41]

Felix Petersen, Amit H Bermano, Oliver Deussen, and Daniel Cohen-Or. 2019. Pix2vex: Image-to-geometry reconstruction using a smooth differentiable renderer. arXiv preprint arXiv:1903.11149 (2019).

[42]

Nikhila Ravi, Jeremy Reizenstein, David Novotny, Taylor Gordon, Wan-Yen Lo, Justin Johnson, and Georgia Gkioxari. 2020. PyTorch3D. https://github.com/facebookresearch/pytorch3d.

[43]

Daniel Rebain, Wei Jiang, Soroosh Yazdani, Ke Li, Kwang Moo Yi, and Andrea Tagliasacchi. 2020. DeRF: Decomposed Radiance Fields. https://arxiv.org/abs/2011.12490 (2020).

[44]

O. Ronneberger, P. Fischer, and T. Brox. 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation. In Medical Image Computing and Computer-Assisted Intervention (MICCAI) (LNCS, Vol. 9351). Springer, 234--241.

[45]

Riccardo Roveri, Lukas Rahmann, Cengiz Oztireli, and Markus Gross. 2018. A network architecture for point cloud classification via automatic depth images generation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4176--4184.

[46]

Shunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima, Angjoo Kanazawa, and Hao Li. 2019a. PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization. https://arxiv.org/abs/1905.05172 (2019).

[47]

Shunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima, Angjoo Kanazawa, and Hao Li. 2019b. PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization. In Proceedings of the IEEE International Conference on Computer Vision. 2304--2314.

[48]

Johannes Lutz Schönberger and Jan-Michael Frahm. 2016. Structure-from-Motion Revisited. In Conference on Computer Vision and Pattern Recognition (CVPR).

[49]

Johannes Lutz Schönberger, Enliang Zheng, Marc Pollefeys, and Jan-Michael Frahm. 2016. Pixelwise View Selection for Unstructured Multi-View Stereo. In European Conference on Computer Vision (ECCV).

[50]

Katja Schwarz, Yiyi Liao, Michael Niemeyer, and Andreas Geiger. 2020. GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis. arXiv:2007.02442 [cs.CV]

[51]

V. Sitzmann, J. Thies, F. Heide, M. Nießner, G. Wetzstein, and M. Zollhöfer. 2019a. DeepVoxels: Learning Persistent 3D Feature Embeddings. In Proceedings of Computer Vision and Pattern Recognition (CVPR 2019).

[52]

Vincent Sitzmann, Michael Zollhöfer, and Gordon Wetzstein. 2019b. Scene representation networks: Continuous 3d-structure-aware neural scene representations. In Advances in Neural Information Processing Systems. 1121--1132.

[53]

Pratul Srinivasan, Boyang Deng, Xiuming Zhang, Matthew Tancik, Ben Mildenhall, and Jonathan Barron. 2020. NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis. https://arxiv.org/abs/2012.03927 (2020).

[54]

Pratul P. Srinivasan, Richard Tucker, Jonathan T. Barron, Ravi Ramamoorthi, Ren Ng, and Noah Snavely. 2019. Pushing the Boundaries of View Extrapolation With Multiplane Images. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR. 175--184.

[55]

Matthew Tancik, Ben Mildenhall, Terrence Wang, Divi Schmidt, Pratul Srinivasan, Jonathan Barron, and Ren Ng. 2020. Learned Initializations for Optimizing Coordinate-Based Neural Representations. https://arxiv.org/abs/2012.02189 (2020).

[56]

A. Tewari, O. Fried, J. Thies, V. Sitzmann, S. Lombardi, K. Sunkavalli, R. Martin-Brualla, T. Simon, J. Saragih, M. Nießner, R. Pandey, S. Fanello, G. Wetzstein, J.-Y. Zhu, C. Theobalt, M. Agrawala, E. Shechtman, D. B Goldman, and M. Zollhöfer. 2020. State of the Art on Neural Rendering. Computer Graphics Forum (EG STAR 2020) (2020).

[57]

Ayush Tewari, Michael Zollhöfer, Pablo Garrido, Florian Bernard, Hyeongwoo Kim, Patrick Pérez, and Christian Theobalt. 2018. Self-supervised Multi-level Face Model Learning for Monocular Reconstruction at over 250 Hz. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[58]

Justus Thies, Michael Zollhöfer, and Matthias Nießner. 2019. Deferred Neural Rendering: Image Synthesis using Neural Textures. ACM Transactions on Graphics 2019 (TOG) (2019).

Digital Library

[59]

Edgar Tretschk, Ayush Tewari, Vladislav Golyanik, Michael Zollhöfer, Christoph Lassner, and Christian Theobalt. 2020. Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Deforming Scene from Monocular Video. arXiv:2012.12247 [cs.CV]

[60]

Alex Trevithick and Bo Yang. 2020. GRF: Learning a General Radiance Field for 3D Scene Representation and Rendering. https://arxiv.org/abs/2010.04595 (2020).

[61]

Richard Tucker and Noah Snavely. 2020. Single-view View Synthesis with Multiplane Images. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[62]

Shubham Tulsiani, Tinghui Zhou, Alexei A Efros, and Jitendra Malik. 2017. Multi-view supervision for single-view reconstruction via differentiable ray consistency. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2626--2634.

[63]

Julien Valentin, Cem Keskin, Pavel Pidlypenskyi, Ameesh Makadia, Avneesh Sud, and Sofien Bouaziz. 2019. TensorFlow Graphics: Computer Graphics Meets Deep Learning.

[64]

Shih-En Wei, Jason Saragih, Tomas Simon, Adam W. Harley, Stephen Lombardi, Michal Perdoch, Alexander Hypes, Dawei Wang, Hernan Badino, and Yaser Sheikh. 2019. VR Facial Animation via Multiview Image Translation. ACM Trans. Graph. 38, 4, Article 67 (July 2019), 16 pages.

Digital Library

[65]

Olivia Wiles, Georgia Gkioxari, Richard Szeliski, and Justin Johnson. 2020. Synsin: End-to-end view synthesis from a single image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 7467--7477.

[66]

Chenglei Wu, Takaaki Shiratori, and Yaser Sheikh. 2018. Deep Incremental Learning for Efficient High-Fidelity Face Tracking. ACM Trans. Graph. 37, 6, Article 234 (Dec. 2018), 12 pages.

Digital Library

[67]

Jiajun Wu, Chengkai Zhang, Tianfan Xue, Bill Freeman, and Josh Tenenbaum. 2016. Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling. In Advances in neural information processing systems. 82--90.

[68]

Wenqi Xian, Jia-Bin Huang, Johannes Kopf, and Changil Kim. 2020. Space-time Neural Irradiance Fields for Free-Viewpoint Video. https://arxiv.org/abs/2011.12950 (2020).

[69]

Wang Yifan, Felice Serena, Shihao Wu, Cengiz Öztireli, and Olga Sorkine-Hornung. 2019. Differentiable surface splatting for point-based geometry processing. ACM Transactions on Graphics (TOG) 38, 6 (2019), 1--14.

Digital Library

[70]

Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa. 2020. pixelNeRF: Neural Radiance Fields from One or Few Images. https://arxiv.org/abs/2012.02190 (2020).

[71]

Tinghui Zhou, Richard Tucker, John Flynn, Graham Fyffe, and Noah Snavely. 2018. Stereo magnification: Learning view synthesis using multiplane images. arXiv preprint arXiv:1805.09817 (2018).

Cited By

Bai SWang TLi CVenkatesh ASimon TCao CSchwartz GSaragih JSheikh YWei S(2024)Universal Facial Encoding of Codec Avatars from VR HeadsetsACM Transactions on Graphics10.1145/365823443:4(1-22)Online publication date: 19-Jul-2024
https://dl.acm.org/doi/10.1145/3658234
Medin SLi GDu RGarbin SDavidson PWornell GBeeler TMeka A(2024)FaceFolds: Meshed Radiance Manifolds for Efficient Volumetric Rendering of Dynamic FacesProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/36513047:1(1-17)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3651304
Teotia KR MPan XKim HGarrido PElgharib MTheobalt C(2024)HQ3DAvatar: High-quality Implicit 3D Head AvatarACM Transactions on Graphics10.1145/364988943:3(1-24)Online publication date: 9-Apr-2024
https://dl.acm.org/doi/10.1145/3649889
Show More Cited By

Index Terms

Mixture of volumetric primitives for efficient neural rendering
1. Computing methodologies
  1. Computer graphics
    1. Rendering

Recommendations

Deferred neural rendering: image synthesis using neural textures

The modern computer graphics pipeline can synthesize images at remarkable visual quality; however, it requires well-defined, high-quality 3D content as input. In this work, we explore the use of imperfect 3D content, for instance, obtained from photo-...
Deferred neural lighting: free-viewpoint relighting from unstructured photographs

We present deferred neural lighting, a novel method for free-viewpoint relighting from unstructured photographs of a scene captured with handheld devices. Our method leverages a scene-dependent neural rendering network for relighting a rough geometric ...
PS-NeRF: Neural Inverse Rendering for Multi-view Photometric Stereo
Computer Vision – ECCV 2022
Abstract
Traditional multi-view photometric stereo (MVPS) methods are often composed of multiple disjoint stages, resulting in noticeable accumulated errors. In this paper, we present a neural inverse rendering method for MVPS based on implicit ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 40, Issue 4

August 2021

2170 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/3450626

Editor:
Sylvain Paris
Adobe Inc.

Issue’s Table of Contents

Copyright © 2021 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 July 2021

Published in TOG Volume 40, Issue 4

Check for updates

Author Tag

neural rendering

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

114
Total Citations
View Citations
2,419
Total Downloads

Downloads (Last 12 months)601
Downloads (Last 6 weeks)50

Reflects downloads up to 15 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Bai SWang TLi CVenkatesh ASimon TCao CSchwartz GSaragih JSheikh YWei S(2024)Universal Facial Encoding of Codec Avatars from VR HeadsetsACM Transactions on Graphics10.1145/365823443:4(1-22)Online publication date: 19-Jul-2024
https://dl.acm.org/doi/10.1145/3658234
Medin SLi GDu RGarbin SDavidson PWornell GBeeler TMeka A(2024)FaceFolds: Meshed Radiance Manifolds for Efficient Volumetric Rendering of Dynamic FacesProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/36513047:1(1-17)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3651304
Teotia KR MPan XKim HGarrido PElgharib MTheobalt C(2024)HQ3DAvatar: High-quality Implicit 3D Head AvatarACM Transactions on Graphics10.1145/364988943:3(1-24)Online publication date: 9-Apr-2024
https://dl.acm.org/doi/10.1145/3649889
Chen YWang LLi QXiao HZhang SYao HLiu Y(2024)MonoGaussianAvatar: Monocular Gaussian Point-based Head AvatarACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657499(1-9)Online publication date: 13-Jul-2024
https://dl.acm.org/doi/10.1145/3641519.3657499
Rao PFox GMeka AB R MZhan FWeyrich TBickel BPfister HMatusik WElgharib MTheobalt C(2024)Lite2Relight: 3D-aware Single Image Portrait RelightingACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657470(1-12)Online publication date: 13-Jul-2024
https://dl.acm.org/doi/10.1145/3641519.3657470
Ma SWeng YShao TZhou K(2024)3D Gaussian Blendshapes for Head Avatar AnimationACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657462(1-10)Online publication date: 13-Jul-2024
https://dl.acm.org/doi/10.1145/3641519.3657462
Jiang YYu CXie TLi XFeng YWang HLi MLau HGao FYang YJiang C(2024)VR-GS: A Physical Dynamics-Aware Interactive Gaussian Splatting System in Virtual RealityACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657448(1-1)Online publication date: 13-Jul-2024
https://dl.acm.org/doi/10.1145/3641519.3657448
Zhao BLi YSun ZZeng LShen YMa RZhang YBao HCui Z(2024)GaussianPrediction: Dynamic 3D Gaussian Prediction for Motion Extrapolation and Free View SynthesisACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657417(1-12)Online publication date: 13-Jul-2024
https://dl.acm.org/doi/10.1145/3641519.3657417
Yang HZheng MMa CLai YWan PHuang H(2024)VRMM: A Volumetric Relightable Morphable Head ModelACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657406(1-11)Online publication date: 13-Jul-2024
https://dl.acm.org/doi/10.1145/3641519.3657406
Edavamadathil Sivaram VLi TRamamoorthi R(2024)Neural Geometry Fields For MeshesACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657399(1-11)Online publication date: 13-Jul-2024
https://dl.acm.org/doi/10.1145/3641519.3657399
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

Media

Figures

Other

Tables

View Issue’s Table of Contents